Adaptive Reversible 3D Model Hiding Method Based on Convolutional Neural Network Prediction Error Expansion

Abstract

Although reversible data hiding technology is widely used, it still faces several challenges and issues. These include ensuring the security and reliability of embedded secret data, improving the embedding capacity, and maintaining the quality of media data. Additionally, irregular data types, such as three-dimensional point clouds and triangle mesh-represented 3D models, lack an ordered structure in their representation. As a result, embedding these irregular data into digital media does not provide sufficient information for the complete recovery of the original data during extraction. To address this issue, this paper proposes a method based on convolutional neural network prediction error expansion to enhance the embedding capacity of carrier images while maintaining acceptable visual quality. The triangle mesh representation of the 3D model is regularized in a two-dimensional parameterization domain, and the regularized 3D model is reversibly embedded into the image. The process of embedding and extracting confidential information in carrier images is symmetrical, and the regularization and restoration of 3D models are also symmetrical. Experiments show that the proposed method increases the reversible embedding capacity, and the triangle mesh can be conveniently subjected to reversible hiding.

Keywords

• reversible data hiding
• surface parameterization
• regular structure
• cnn
• 3d model